Digitalisation Pursuits Start with Sound Data Governance Strategy

Digitalisation initiatives truly run across financial services in a way few other technology trends do. Spanning both investment banks and asset managers, they often serve as a kind of umbrella that allows a firm to fold in multiple institutional priorities—increased processes automation, heightened regulatory compliance, and emerging technologies, among others—all at once. That can sometimes make them tricky to pin down and project-manage successfully; yet we’ve seen time and again that few ventures can make a bigger difference at a functional and enterprise level. And that is particularly so for a firm’s relationship with its data. 

Financial Information Management Association's (FIMA) 2018 research exposed why undergirding digitalisation with sound data governance strategy and technology platforms is crucial today. Increasingly, this is a natural consequence of evolving objectives, and realizing the value in the data, itself — for example, isolating and analyzing client behavior to more quickly (re)align services and investments to their needs. Still, many other digitisation projects continue to work on automation that is behind the scenes, whether standardizing and reengineering trading operations across desks, or introducing new robotics process automation (RPA) for routine data scrubbing. Sometimes, good data is the means to an end; other times it is the end, in itself.

Countervailing Forces

Either way, looking at digitalisation through a data prism introduces a number of interesting challenges, as highlighted in the survey. The introduction of Europe’s GDPR privacy rules has completely upended firms’ perspectives on personal data—with an overwhelming 69 percent of those responding saying this area represent their highest area of regulatory complexity.

GDPR is primarily a data governance issue, because it asks covered institutions to map, risk-rate, control, and report upon every touch-point for client data—capabilities that many firms still lack. “Many organisations had [personal data] separate infrastructures in place,” as Mizuho CDO Gary Goldberg puts it, indicating it is primarily a refresh and integration challenge. But others speaking to researchers highlighted a more colorful variety of problems—from lack of policy clarity and perceived contradictions, to the inability to “be forgotten” via erasure personal-identifying information from distributed ledgers. Many seemed to view GDPR as throwing the industry in reverse, just as it is really kicking into innovation. Data teams must find a way to reconcile those countervailing forces. 

Indeed, even putting aside regulatory compulsion, a related priority identified in the FIMA research is more organic enablement of emerging technologies. Respondents pointed to a wide range of new applications that they will invest in over the next two years: 5 out of 6 said machine learning is on tap while almost three quarters (72 percent) pointed to other artificial intelligence. Two thirds will deploy blockchain and almost half (44 percent) are looking to RPA.

Rising Ambitions

But slightly above all of these is data management technology itself—at 89 percent. ING Group CDO Chris Bannocks said the linkage here is obvious. “All of these underlying technologies help data management, and data management helps many of the other technologies,” he argued. “Machine Learning and AI require data in a certified state, unless you burden those technologies with data quality work.”

But another reason plays out in the numbers, too. Firms are using digitalisation impetus for data governance because they generally feel their capabilities lag behind their ambitions. For instance, only 13 percent rated their data governance in the highest possible category for effectiveness, while nearly half (48 percent) said they are acutely or very aware of the gaps in their data infrastructure. Institutional visibility of the issue is rising as more is being asked of the data stack today, and Bannocks posits that “the target end-state for the [chief] data office in many banks” is analytics-driven value extraction. Already, a strong and surprisingly high number—in fact, a majority—of respondents say that “all” data management decisions are now made with this end-objective in mind.

The FIMA report touches on a number of additional areas related to digitalisation, including assessment of vendor relationships and the rising importance of industry utilities and mutualization. Overall, however, the research makes clear that digitalisation projects are taking their data governance aspects (and problems) more seriously than ever before. Sound strategy—including higher attention paid to data integrity—is core to these transformation initiatives delivering on their promise.